Suppose you are given a set of a few qubits, which are connected in some arbitrary fashion. You are able to apply a certain set of gates and measurements. The qubits are subject to noise, and your goal is to preserve as well as possible the quantum information that is initially stored inside one of the qubits. How do you proceed? Of course well-known approaches exist (stabilizer codes, decoherence- free subspaces, adaptive phase estimation etc.), but the optimal strategy depends very much on the details of the hardware and the noise. It would be best to have a fully autonomous way to discover strategies, where these strategies will also involve feedback, i.e. adaptive responses to measurement results. This is a situation which is perfectly suited for reinforcement learning with neural networks - a technique that has shown spectacular successes in recent years, such as beating human champions in the game of Go. We have applied the technique for the first time to quantum physics, specifically quantum error correction, and in this talk I will present our results as well as the conceptual innovations needed to make it work.
By Florian Marquardt
By Gary Steele
By Hendrik Bluhm
By Jean-Michel Raimond
By Alain Sarlette
By Sylvain Schwartz
By Sophia Economou
By Klaus Mølmer
By Howard M. Wiseman
By Michel Devoret
By Akira Furusawa
By Yanhong Xiao
By Alex Retzker
By Nicolas Didier
By Ivan Deutsch
By Mankei Tsang
By Aashish Clerk
By Valentina Parigi
By Rebing Wu
By Michael Biercuk
By Thomas Ayral
By Yassine Hamoudi
By Yassine Hamoudi
By Nana Liu
By Nana Liu
